Climate causes shifts in grey seal phenology by modifying age structure

There are numerous examples of phenological shifts that are recognized both as indicators of climate change and drivers of ecosystem change. A pressing challenge is to understand the causal mechanisms by which climate affects phenology. We combined annual population census data and individual longitudinal data (1992–2018) on grey seals, Halicheorus grypus , to quantify the relationship between pupping season phenology and sea surface temperature. A temperature increase of 2°C was associated with a pupping season advance of approximately seven days at the population level. However, we found that maternal age, rather than sea temperature, accounted for changes in pupping date by individuals. Warmer years were associated with an older average age of mothers, allowing us to explain phenological observations in terms of a changing population age structure. Finally, we developed a matrix population model to test whether our observations were consistent with changes to the stable age distribution. This could not fully account for observed phenological shift, strongly suggesting transient modification of population age structure, for example owing to immigration. We demonstrate a novel mechanism for phenological shifts under climate change in long-lived, age- or stage-structured species with broad implications for dynamics and resilience, as well as population management.

Demographic and epidemiological decomposition analysis of global changes in suicide rates and numbers over the period 1990–2019

BackgroundSuicide presents an ongoing public health challenge internationally. Nearly 800 000 people around the world lose their life to suicide every year, and many more attempt suicide.MethodsA decomposition analysis was performed using global suicide mortality and population data from the Global Burden of Disease Study 2019.ResultsDespite a significant decrease in age-specific suicide rate between 1990 and 2019 (-4.01; from 13.8% to 9.8% per 100 000), the overall numbers of suicide deaths increased by 19 897 (from 738 799 to 758 696) in the same time period. The reductions in age-specific suicide rates (−6.09; 152%) contributed to the overall reductions in suicide rates; however, this was offset by overtime changes in population age structure (2.08; −52%). The increase in suicide numbers was partly attributable to population growth (300 942; 1512.5%) and population age structure (189 512; 952.4%), which was attenuated by the significant reduction in overall suicide rates (−470 556; 2364.9%). The combined effect of these factors varied across the World Bank income level regions. For example, in the upper-middle-income level region, the effect of the reduction in age-specific suicide rates (−289 731; −1456.1%) exceeded the effect of population age structure (124 577; 626.1%) and population growth (83 855; 421.4%), resulting in its substantial decline in total suicide deaths (−81 298; −408.6%). However, in lower-middle income region, there was a notable increase in suicide death (72 550; 364.6%), which was related to the net gain of the reduction in age-specific suicide rates (-115 577; -580.9%) and negated by the increase in the number of suicide deaths due to population growth (152 093; 764.4%) and population age structure (36 034; 181.1%).ConclusionMore support and resources should be deployed for suicide prevention to the low-income and middle-income regions in order to achieve the reduction goal. Moreover, suicide prevention among older adults is increasingly critical given the world’s rapidly ageing populations in all income level regions.

Polarized nature of the COVID-19 pandemic in Japan: associations with population age structure and behaviours

Background Although the scale of the coronavirus disease (COVID-19) pandemic was relatively small in Japan compared with the rest of the world, the polarisation of areas into high- and low-COVID-19-incidence areas was observed among the 47 prefectures. The aims of this study were not only identifying the factors associated with the polarised COVID-19 pandemic in Japan but also discussing effective preventive measures. Methods This was an ecological study using online survey data which was cross-sectionally conducted by the author. A total of 6000 respondents who resided in 10 low- and 10 high-COVID-19 incidence prefectures, with a wide gap in terms of COVID-19 incidence, in Japan were recruited. Data on COVID-19 cases and geodemographic information were obtained from official government sites. Statistical analyses were conducted to compare variables between the two areas and age groups. Results This study revealed that that age influenced people’s behaviours and perceptions, except one behaviour of ‘wearing facemasks’. The major factors significantly associated with the cumulative number of COVID-19 cases per 100,000 people were ‘commuting by private automobile’ (adjusted odds ratio [AOR], 0.444; 95% confidence interval [CI], 0.394–0.501), ‘commuting by public transportation’ (AOR, 6.813; 95% CI, 5.567–8.336), ‘washing hands’ (AOR, 1.233; 95% CI, 1.005–1.511), ‘opening windows regularly’ (AOR, 1.248; 95% CI, 1.104–1.412), ‘avoiding crowded places (AOR, 0.757; 95% CI, 0.641–0.893), ‘non-scheduled visits to drinking places’ (AOR, 1.212; 95% CI, 1.054–1.392) and ‘perceived risk of contracting COVID-19’ (AOR, 1.380; 95% CI, 1.180–1.612). These factors were strongly associated with age groups. Conclusions Effective preventive measures for COVID-19 transmission can be developed by understanding the characteristics of populated areas, such as public transportation infrastructure and younger people’s movements and behaviours in relation to the population age structure to contain the current epidemic and protect the most vulnerable elderly people.

Intraday Variation Mapping of Population Age Structure via Urban-Functional-Region-Based Scaling

The spatial distribution of the population is uneven for various reasons, such as urban-rural differences and geographical conditions differences. As the basic element of the natural structure of the population, the age structure composition of populations also varies considerably across the world. Obtaining accurate and spatiotemporal population age structure maps is crucial for calculating population size at risk, analyzing populations mobility patterns, or calculating health and development indicators. During the past decades, many population maps in the form of administrative units and grids have been produced. However, these population maps are limited by the lack of information on the change of population distribution within a day and the age structure of the population. Urban functional regions (UFRs) are closely related to population mobility patterns, which can provide information about population variation intraday. Focusing on the area within the Beijing Fifth Ring Road, the political and economic center of Beijing, we showed how to use the temporal scaling factors obtained by analyzing the population survey sampling data and population dasymetric maps in different categories of UFRs to realize the intraday variation mapping of elderly individuals and children. The population dasymetric maps were generated on the basis of covariates related to population. In this article, 50 covariates were calculated from remote sensing data and geospatial data. However, not all covariates are associate with population distribution. In order to improve the accuracy of dasymetric maps and reduce the cost of mapping, it is necessary to select the optimal subset for the dasymetric model of elderly and children. The random forest recursive feature elimination (RF-RFE) algorithm was introduced to obtain the optimal subset of different age groups of people and generate the population dasymetric model in this article, as well as to screen out the optimal subset with 38 covariates and 26 covariates for the dasymetric models of the elderly and children, respectively. An accurate UFR identification method combining point of interest (POI) data and OpenStreetMap (OSM) road network data is also introduced in this article. The overall accuracy of the identification results of UFRs was 70.97%, which is quite accurate. The intraday variation maps of population age structure on weekdays and weekends were made within the Beijing Fifth Ring Road. Accuracy evaluation based on sampling data found that the overall accuracy was relatively high—R2 for each time period was higher than 0.5 and root mean square error (RMSE) was less than 0.05. On weekdays in particular, R2 for each time period was higher than 0.61 and RMSE was less than 0.02.

Modeling schistosomiasis transmission: the importance of snail population structure

Background Schistosomiasis is a neglected tropical disease endemic in 54 countries. A major Schistosoma species, Schistosoma mansoni, is sustained via a life cycle that includes both human and snail hosts. Mathematical models of S. mansoni transmission, used to elucidate the complexities of the transmission cycle and estimate the impact of intervention efforts, often focus primarily on the human host. However, S. mansoni incurs physiological costs in snails that vary with the age of the snail when first infected. Snail demography and the age of snail infection could thus affect the force of infection experienced by humans, which is frequently used to predict the impact of various control strategies. Methods To address how these snail host and parasite interactions influence model predictions, we developed deterministic models of schistosomiasis transmission that include varying complexity in the snail population age structure. Specifically, we examined how model outputs, such as schistosome prevalence in human and snail populations, respond to the inclusion of snail age structure. Results Our models suggest that snail population age structure modifies the force of infection experienced by humans and the relationship between snail infection prevalence and corresponding human infection prevalence. There are significant differences in estimated snail infection, cercarial density and mean worm burden between models without snail population dynamics and those with snail populations, and between models with a homogeneous snail population and those with age stratification. The variation between finely age-stratified snail populations and those grouped into only juvenile and adult life stages is, however, minimal. Conclusions These results indicate that including snails and snail age structure in a schistosomiasis transmission model alters the relationship between snail and human infection prevalence. This highlights the importance of accounting for a heterogeneous intermediate host population in models of schistosomiasis transmission where the impact of proposed control measures is being considered.